Visualizing and updating long-term memory percepts in a video surveillance system

ABSTRACT

Techniques are disclosed for visually conveying a percept. The percept may represent information learned by a video surveillance system. A request may be received to view a percept for a specified scene. The percept may have been derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. A visual representation of the percept may be generated. A user interface may be configured to display the visual representation of the percept and to allow a user to view and/or modify metadata attributes with the percept. For example, the user may label a percept and set events matching the percept to always (or never) result in alert being generated for users of the video surveillance system.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the invention provide techniques for conveyinginformation learned by a video surveillance system. More specifically,embodiments of the invention relate to techniques for visualizing andupdating long-term memory percepts in a video surveillance system.

2. Description of the Related Art

Some currently available video surveillance systems provide simpleobject recognition capabilities. For example, a video surveillancesystem may be configured to classify a group of pixels (referred to as a“blob”) in a given frame as being a particular object (e.g., a person orvehicle). Once identified, a “blob” may be tracked frame-to-frame inorder to follow the “blob” moving through the scene over time, e.g., aperson walking across the field of vision of a video surveillancecamera. Further, such systems may be configured to determine when anobject has engaged in certain predefined behaviors.

However, such surveillance systems typically require that the objectsand/or behaviors which may be recognized by the system to be defined inadvance. Thus, in practice, these systems rely on predefined definitionsfor objects and/or behaviors to evaluate a video sequence. In otherwords, unless the underlying system includes a description for aparticular object or behavior, the system is generally incapable ofrecognizing that behavior (or at least instances of the patterndescribing the particular object or behavior). Thus, what is “normal” or“abnormal” behavior needs to be defined in advance, and separatesoftware products need to be developed to recognize additional objectsor behaviors. This results in surveillance systems with recognitioncapabilities that are labor intensive and prohibitively costly tomaintain or adapt for different specialized applications. Accordingly,currently available video surveillance systems are typically unable torecognize new patterns of behavior that may emerge in a given scene orrecognize changes in existing patterns. More generally, such systems areoften unable to identify objects, events, behaviors, or patterns asbeing “normal” or “abnormal” by observing what happens in the scene overtime; instead, such systems rely on static patterns defined in advance.

SUMMARY OF THE INVENTION

One embodiment of the invention includes a method for a videosurveillance system to process a sequence of video frames depicting ascene captured by a video camera. The method may generally includereceiving a request to view a visual representation of a percept encodedin a long-term memory of a machine-learning engine. The precept may beused to encode a pattern of behavior learned by the machine-learningengine from analyzing data streams generated from the sequence of videoframes. The method may also include retrieving the requested perceptfrom the long-term memory of the machine-learning engine. The long-termmemory stores a plurality of percepts. The method may also includegenerating a visual representation of the requested percepts. The visualrepresentation presents a directed graph representing the pattern ofbehavior encoded by the requested percept.

Additionally, nodes in the directed graph may be used to represents oneor more primitive events observed by the video surveillance system inthe sequence of video frames and each links between nodes may representa relationship between primitive events in the pattern of behavior.

Another embodiment of the invention includes a computer-readable storagemedium containing a program which, when executed by a video surveillancesystem, performs an operation to process a sequence of video framesdepicting a scene captured by a video camera. The operation maygenerally include receiving a request to view a visual representation ofa percept encoded in a long-term memory of a machine-learning engine.The precept may be used to encode a pattern of behavior learned by themachine-learning engine from analyzing data streams generated from thesequence of video frames. The operation may also include retrieving therequested percept from the long-term memory of the machine-learningengine. The long-term memory may store a plurality of percepts. Theoperation may further include generating a visual representation of therequested percept. In general, the visual representation presents adirected graph representing the pattern of behavior encoded by therequested percept.

Still another embodiment of the invention provides a video surveillancesystem. The video surveillance system may generally include a videoinput source configured to provide a sequence of video frames, eachdepicting a scene. The video surveillance system may also include aprocessor and a memory containing a program, which when executed by theprocessor is configured to perform an operation to process the scenedepicted in the sequence of video frames. The operation may generallyinclude receiving a request to view a visual representation of a perceptencoded in a long-term memory of a machine-learning engine. The preceptmay be used to encode a pattern of behavior learned by themachine-learning engine from analyzing data streams generated from thesequence of video frames. The operation may also include retrieving therequested percept from the long-term memory of the machine-learningengine. The long-term memory may store a plurality of percepts. Theoperation may further include generating a visual representation of therequested percept. In general, the visual representation presents adirected graph representing the pattern of behavior encoded by therequested percept.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features, advantages, andobjects of the present invention are attained and can be understood indetail, a more particular description of the invention, brieflysummarized above, may be had by reference to the embodiments illustratedin the appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 illustrates components of a video analysis andbehavior-recognition system, according to one embodiment of theinvention.

FIG. 2 further illustrates components of a computer vision engine and amachine-learning engine, according to one embodiment of the invention.

FIG. 3 illustrates a sequence of interactions between a transactionserver and a client, according to one embodiment of the invention.

FIG. 4 illustrates a graphical user interface (GUI) conveying a percept,according to one embodiment of the invention.

FIG. 5 illustrates a method for generating a visualization of a percept,according to one embodiment of the invention.

FIG. 6 illustrates a method for modifying metadata properties of apercept, according to one embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Embodiments of the invention provide an interface configured to visuallyconvey information learned by a behavior-recognition system. Thebehavior-recognition system may be configured to identify, learn, andrecognize patterns of behavior by observing and evaluating eventsdepicted by a sequence of video frames. In a particular embodiment, thebehavior-recognition system may include both a computer vision engineand a machine learning engine. The computer vision engine may beconfigured to evaluate a stream of video frames. Typically, each frameof video may be characterized using multiple color (or grayscale)channels (e.g., a radiance value between 0-255 and a set of red, green,and blue (RGB) color channels values, each between 0-255). Further, thecomputer vision engine may generate a background image by observing thescene over a number of video frames. For example, consider a videocamera trained on a stretch of a highway. In such a case, the backgroundwould include the roadway surface, the medians, any guard rails or othersafety devices, and traffic control devices, etc., that are visible tothe camera. Vehicles traveling on the roadway (and any other person orthing engaging in some activity) that are visible to the camera wouldrepresent scene foreground objects.

The computer vision engine may compare the pixel values for a givenframe with the background image and identify objects as they appear andmove about the scene. Typically, when a region of the scene (referred toas a “blob” or “patch”) is observed with appearance values that differsubstantially from the background image, that region is identified asdepicting a foreground object. Once identified, the object may beevaluated by a classifier configured to determine what is depicted bythe foreground object (e.g., a vehicle or a person). Further, thecomputer vision engine may identify features (e.g., height/width inpixels, average color values, shape, area, and the like) used to trackthe object from frame-to-frame. Further still, the computer visionengine may derive a variety of information while tracking the objectfrom frame-to-frame, e.g., position, current (and projected) trajectory,direction, orientation, velocity, acceleration, size, color, and thelike. In one embodiment, the computer vision outputs this information asa stream of “context events” describing a collection of kinematicinformation related to each foreground object detected in the videoframes. Each context event may provide kinematic data related to aforeground object observed by the computer vision engine in the sequenceof video frames.

Data output from the computer vision engine may be supplied to themachine learning engine. In one embodiment, the machine learning enginemay evaluate the context events to generate “primitive events”describing object behavior. Each primitive event may provide semanticmeaning to a group of one or more context events. For example, assume acamera records a car entering a scene, and that the car turns and parksin a parking spot. In such a case, the computer vision engine couldinitially recognize the car as a foreground object; classify it as beinga vehicle, and output kinematic data describing the position, movement,speed, etc., of the car in the context event stream. In turn, aprimitive event detector could generate a stream of primitive eventsfrom the context event stream such as “vehicle appears,” vehicle turns,”“vehicle slowing,” and “vehicle stops” (once the kinematic informationabout the car indicated a speed of 0). As events occur, and re-occur,the machine learning engine may create, encode, store, retrieve, andreinforce patterns representing the events observed to have occurred,e.g., long-term memories (or long-term “percepts”) representing ahigher-level abstraction of a car parking in the scene—generated fromthe primitive events underlying multiple observations of different carsentering and parking. The interface may be configured to visually conveysuch patterns. Specifically, the patterns may be stored in a long-termmemory of the machine learning engine. Further still, patternsrepresenting an anomalous event (relative to prior observation) orevents identified as an event of interest may result in alerts passed tousers of the behavioral recognition system.

In one embodiment, the machine learning engine may also include atransaction server. The transaction server may generate a visualrepresentation of percepts encoded in the long-term memory of themachine-learning engine. Thus, the transaction server allows users toexplore data learned by the machine-learning engine. Further, thetransaction server allows users to supply metadata specifying how thesystem should respond to certain observed events and/or behaviors (forexample, when to produce (or not produce) an alert). For example, thetransaction server may receive a request to view percepts stored in thelong-term memory generated through observations of a scene over time. Apercept may include one or more context events generalizing observationsof multiple foreground objects of a scene over time. In response, thetransaction server may generate a visual representation of the perceptretrieved from the long-term memory. Further, the transaction server mayalso receive user requests to associate metadata with a perceptretrieved from the long-term memory. The metadata may be used to guidesystem behavior. For example, a user may provide a name for percept,specify a rule that the system should generate an alert (or refrain fromgenerating an alert) when a sequence of events that match a percept isobserved, or otherwise modify metadata associated with a percept encodedin the long-term memory.

In the following, reference is made to embodiments of the invention.However, it should be understood that the invention is not limited toany specifically described embodiment. Instead, any combination of thefollowing features and elements, whether related to differentembodiments or not, is contemplated to implement and practice theinvention. Furthermore, in various embodiments the invention providesnumerous advantages over the prior art. However, although embodiments ofthe invention may achieve advantages over other possible solutionsand/or over the prior art, whether or not a particular advantage isachieved by a given embodiment is not limiting of the invention. Thus,the following aspects, features, embodiments and advantages are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s). Likewise,reference to “the invention” shall not be construed as a generalizationof any inventive subject matter disclosed herein and shall not beconsidered to be an element or limitation of the appended claims exceptwhere explicitly recited in a claim(s).

One embodiment of the invention is implemented as a program product foruse with a computer system. The program(s) of the program productdefines functions of the embodiments (including the methods describedherein) and can be contained on a variety of computer-readable storagemedia. Examples of computer-readable storage media include (i)non-writable storage media (e.g., read-only memory devices within acomputer such as CD-ROM or DVD-ROM disks readable by an optical mediadrive) on which information is permanently stored; (ii) writable storagemedia (e.g., floppy disks within a diskette drive or hard-disk drive) onwhich alterable information is stored. Such computer-readable storagemedia, when carrying computer-readable instructions that direct thefunctions of the present invention, are embodiments of the presentinvention. Other examples media include communications media throughwhich information is conveyed to a computer, such as through a computeror telephone network, including wireless communications networks.

In general, the routines executed to implement the embodiments of theinvention may be part of an operating system or a specific application,component, program, module, object, or sequence of instructions. Thecomputer program of the present invention is comprised typically of amultitude of instructions that will be translated by the native computerinto a machine-readable format and hence executable instructions. Also,programs are comprised of variables and data structures that eitherreside locally to the program or are found in memory or on storagedevices. In addition, various programs described herein may beidentified based upon the application for which they are implemented ina specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature.

FIG. 1 illustrates components of a video analysis andbehavior-recognition system 100, according to one embodiment of theinvention. As shown, the behavior-recognition system 100 includes avideo input source 105, a network 110, a computer system 115, and inputand output devices 118 (e.g., a monitor, a keyboard, a mouse, a printer,and the like). The network 110 may transmit video data recorded by thevideo input 105 to the computer system 115. Illustratively, the computersystem 115 includes a CPU 120, storage 125 (e.g., a disk drive, opticaldisk drive, floppy disk drive, and the like), and a memory 130containing both a computer vision engine 135 and a machine-learningengine 140. As described in greater detail below, the computer visionengine 135 and the machine-learning engine 140 may provide softwareapplications configured to process a sequence of video frames providedby the video input source 105.

Network 110 receives video data (e.g., video stream(s), video images, orthe like) from the video input source 105. The video input source 105may be a video camera, a VCR, DVR, DVD, computer, web-cam device, or thelike. For example, the video input source 105 may be a stationary videocamera aimed at a certain area (e.g., a subway station, a parking lot, abuilding entry/exit, etc.), which records the events taking placetherein. Generally, the area visible to the camera is referred to as the“scene.” The video input source 105 may be configured to record thescene as a sequence of individual video frames at a specified frame-rate(e.g., 24 frames per second), where each frame includes a fixed numberof pixels (e.g., 320×240). Each pixel of each frame may specify a colorvalue (e.g., an RGB value) or grayscale value (e.g., a radiance valuebetween 0-255). Further, the video stream may be formatted using knownsuch formats e.g., MPEG2, MJPEG, MPEG4, H.263, H.264, and the like.Additionally, although shown as a single video input source 105, thesystem 100 may support many video cameras—each observing a distinctscene. Further, each camera may have multiple preset positions (i.e., asingle camera may, in fact, be trained on more than one scene). In suchcases, a separate instance of the computer vision engine 135 and machinelearning-engine 140 may be available to observe the video stream fromeach camera (and presets, if any).

The computer vision engine 135 may be configured to analyze this rawinformation to identify active objects in the video stream, classify theobjects, derive a variety of metadata regarding the actions andinteractions of such objects, and supply this information to amachine-learning engine 140. In turn, the machine-learning engine 140may be configured to evaluate, observe, learn, and remember detailsregarding events (and types of events) that transpire within the sceneover time.

In one embodiment, the machine-learning engine 140 receives the videoframes and the data generated by the computer vision engine 135. Themachine-learning engine 140 may be configured to analyze the receiveddata, build semantic representations of events depicted in the videoframes, detect patterns, and, ultimately, to learn from these observedpatterns to identify normal and/or abnormal events. Additionally, datadescribing whether a normal/abnormal behavior/event has been determinedand/or what such behavior/event is may be provided to output devices 118to issue alerts, for example, an alert message presented on a GUIscreen. In general, the computer vision engine 135 and themachine-learning engine 140 both process video data in real-time.However, time scales for processing information by the computer visionengine 135 and the machine-learning engine 140 may differ. For example,in one embodiment, the computer vision engine 135 processes the receivedvideo data frame-by-frame, while the machine-learning engine 140processes data every N-frames. In other words, while the computer visionengine 135 analyzes each frame in real-time to derive a set ofinformation about what is occurring within a given frame, themachine-learning engine 140 is not constrained by the real-time framerate of the video input.

Note, however, FIG. 1 illustrates merely one possible arrangement of thebehavior-recognition system 100. For example, although the video inputsource 105 is shown connected to the computer system 115 via the network110, the network 110 is not always present or needed (e.g., the videoinput source 105 may be directly connected to the computer system 115).Further, various components and modules of the behavior-recognitionsystem 100 may be implemented in other systems. For example, in oneembodiment, the computer vision engine 135 may be implemented as a partof a video input device (e.g., as a firmware component wired directlyinto a video camera). In such a case, the output of the video camera maybe provided to the machine-learning engine 140 for analysis. Similarly,the output from the computer vision engine 135 and machine-learningengine 140 may be supplied over computer network 110 to other computersystems. For example, the computer vision engine 135 andmachine-learning engine 140 may be installed on a server system andconfigured to process video from multiple input sources (i.e., frommultiple cameras). In such a case, a client application running onanother computer system may request (or receive) the results of overnetwork 110.

FIG. 2 further illustrates components of the computer vision engine 135and the machine-learning engine 140 first illustrated in FIG. 1,according to one embodiment of the invention. As shown, the computervision engine 135 includes a background/foreground (BG/FG) component205, a tracker component 210, an estimator/identifier component 215, anda context processor component 220. Collectively, the components 205,210, 215, and 220 provide a pipeline for processing an incoming sequenceof video frames supplied by the video input source 105 (indicated by thesolid arrows linking the components). Additionally, the output of onecomponent may be provided to multiple stages of the component pipeline(as indicated by the dashed arrows) as well as to the machine-learningengine 140. In one embodiment, the components 205, 210, 215, and 220 mayeach provide a software module configured to provide the functionsdescribed herein. Of course one of ordinary skill in the art willrecognize that the components 205, 210, 215, and 220 may be combined (orfurther subdivided) to suit the needs of a particular case.

In one embodiment, the BG/FG component 205 may be configured to separateeach frame of video provided by the video input source 105 into astationary or static part (the scene background) and a collection ofvolatile parts (the scene foreground.) The frame itself may include atwo-dimensional array of pixel values for multiple channels (e.g., RGBchannels for color video or grayscale channel or radiance channel forblack and white video). For example, the BG/FG component 205 may modelthe background states for each pixel using an adaptive resonance theory(ART) network. That is, each pixel may be classified as depicting sceneforeground or scene background using an ART network modeling a givenpixel.

Additionally, the BG/FG component 205 may be configured to generate amask used to identify which pixels of the scene are classified asdepicting foreground and, conversely, which pixels are classified asdepicting scene background. The BG/FG component 205 then identifiesregions of the scene that contain a portion of scene foreground(referred to as a foreground “blob” or “patch”) and supplies thisinformation to subsequent stages of the pipeline. Pixels classified asdepicting scene background maybe used to generate a background imagemodeling the background of scene.

The tracker component 210 may receive the foreground patches produced bythe BG/FG component 205 and generate computational models for thepatches. The tracker component 210 may be configured to use thisinformation, and each successive frame of raw-video, to attempt to trackthe motion of the objects depicted by the foreground patches as theymove about the scene. More simply, the tracker attempts to relate thedepiction of a particular object in one frame to the depiction of thatobject in subsequent frames as it moves throughout the scene.

The estimator/identifier component 215 may receive the output of thetracker component 210 (and the BF/FG component 205) and classify eachtracked object as being one of a known category of objects. For example,in one embodiment, estimator/identifier component 215 may include atrained classifier configured to classify a tracked object as being a“person,” a “vehicle,” an “unknown,” or an “other.” In this context, theclassification of “other” represents an affirmative assertion that theobject is neither a “person” nor a “vehicle.” Additionally, theestimator/identifier component may identify characteristics of thetracked object, e.g., for a person, a prediction of gender, anestimation of a pose (e.g., standing or sitting), or an indication ofwhether the person is carrying an object. Alternatively, theestimator/identifier component 215 may include an unsupervisedclassifier configured to determine a collection of micro features (e.g.,size, color, shininess, rigidity, etc.) and classify observed objectssharing a similar set of micro features as depicting an object of thesame type.

The context processor component 220 may receive the output from otherstages of the pipeline (i.e., the tracked objects, the background andforeground models, and the results of the estimator/identifier component215). Using this information, the context processor 220 may beconfigured to generate a stream of context events regarding objects thathave been tracked (by tracker component 210) and classified (byestimator identifier component 215). For example, the context processorcomponent 220 may evaluate a foreground object from frame-to-frame andoutput context events describing that object's height, width (inpixels), position (as a 2D coordinate in the scene), acceleration,velocity, orientation angle, etc.

The computer vision engine 135 may take the outputs of the components205, 210, 215, and 220 describing the motions and actions of the trackedobjects in the scene and supply this information to the machine-learningengine 140. In one embodiment, the primitive event detector 212 may beconfigured to receive the output of the computer vision engine 135(i.e., the video images, the object classifications, and context eventstream) and generate a sequence of primitive events—labeling theobserved actions or behaviors in the video with semantic meaning. Forexample, assume the computer vision engine 135 has identified aforeground object and classified that foreground object as being avehicle and the context processor component 220 estimates the kinematicdata regarding the car's position and velocity. In such a case, thisinformation is supplied to the machine-learning engine 140 and theprimitive event detector 212. In turn, the primitive event detector 212may generate a semantic symbol stream providing a simple linguisticdescription of actions engaged in by the vehicle. For example, asequence of primitive events related to observations of the computervision engine 135 occurring at a parking lot could include “vehicleappears in scene,” “vehicle moves to a given location,” “vehicle stopsmoving,” “person appears proximate to vehicle,” “person moves,” personleaves scene” “person appears in scene,” “person moves proximate tovehicle,” “person disappears,” “vehicle starts moving,” and “vehicledisappears.” As described in greater detail below, the primitive eventstream may be used to excite the perceptual associative memory 230.

Illustratively, the machine-learning engine 140 includes a long-termmemory 225, a perceptual memory 230, an episodic memory 235, a workspace240, codelets 245, and a mapper component 211. In one embodiment, theperceptual memory 230, the episodic memory 235, and the long-term memory225 are used to identify patterns of behavior, evaluate events thattranspire in the scene, and encode and store observations. Generally,the perceptual memory 230 receives the output of the computer visionengine 135 (e.g., the context event stream) and a primitive event streamgenerated by primitive event detector 212. In one embodiment, theperceptual memory 230 may be implemented as a neural network having agraph of nodes and weighted links between nodes. In such a case, theinput from the computer vision engine 135 is used to excite theperceptual memory 230, and the resulting sub-graph (i.e., a percept) iscopied to the episodic memory 235 as a currently observed event. Thus,each percept may define a sub-graph of a neural network, where each nodeof the graph represents a primitive event (or combination of primitiveevents) and links between nodes represent relationships betweenprimitive events.

The episodic memory 235 stores the percept, which represents observedevents with details related to a particular episode, e.g., informationdescribing time and space details related on an event. That is, theepisodic memory 235 may encode specific details of a particular event,i.e., “what and where” an observed event occurred within a scene.

The long-term memory 225 may store percepts generalizing events observedin the scene. To continue with the example of a vehicle parking, thelong-term memory 225 may encode percepts capturing observations andgeneralizations learned by an analysis of the behavior of objects in thescene such as “vehicles tend to park in a particular place in thescene,” “when parking vehicles tend to move a certain speed,” and “aftera vehicle parks, people tend to appear in the scene proximate to thevehicle,” etc. Thus, the long-term memory 225 stores observations aboutwhat happens within a scene with much of the particular episodic detailsstripped away. In this way, when a new event occurs, percepts theepisodic memory 235 and the long-term memory 225 may be used to relateand understand a current event, i.e., the new event may be compared withpast experience, leading to both reinforcement, decay, and adjustmentsto the percepts stored in the long-term memory 225, over time. In aparticular embodiment, the long-term memory 225 may be implemented as anART network and a sparse-distributed memory data structure.

The mapper component 211 may receive the context event stream and theprimitive event stream and parse information to multiple ART networks togenerate statistical models of what occurs in the scene for differentgroups of context events and primitive events.

Generally, the workspace 240 provides a computational engine for themachine-learning engine 140. For example, the workspace 240 may beconfigured to copy percepts from the perceptual memory 230, retrieverelevant memories from the episodic memory 235 and the long-term memory225, select and invoke the execution of one of the codelets 245. In oneembodiment, each codelet 245 is a software program configured toevaluate different sequences of events and to determine how one sequencemay follow (or otherwise relate to) another (e.g., a finite statemachine). More generally, the codelet may provide a software moduleconfigured to detect interesting patterns from the streams of datasupplied to the machine-learning engine 140. In turn, the codelet 245may create, retrieve, reinforce, or modify metadata related to perceptsin the episodic memory 235 and the long-term memory 225. By schedulingcodelets 245 for execution, copying percepts to/from the workspace 240,the machine-learning engine 140 performs a cognitive cycle used toobserve, and learn, about patterns of behavior that occur within thescene.

As shown in FIG. 2, the machine-learning engine 140 also includes atransaction server 260 and a GUI interface 270. In one embodiment, thetransaction server 260 and GUI tool 270 allow users to retrieve andgenerate visualizations of percepts encoded by the long term memory 225.For example, the transaction server 260 may be configured to processesuser requests from the GUI tool 270 to generate and display avisualization of a percept encoded in the long-term memory. Table Ishows examples of transactions that may be supported by the transactionserver 260:

TABLE I Transactions supported by the transaction server TransactionDescription Get preset list Obtain a list of presets for a specifiedcamera Get list of percepts Obtain a list of percepts for a specifiedpreset Get percept Obtains data for a specified percept (e.g., includingproperties) Set always alert Specifies to always alert on event(s)matching a specified percept Set always ignore Specifies to never alerton event(s) matching a specified percept Modify metadata property Modifya specified property of a perceptNote, while FIG. 2 shows the transaction server 260 as being separatefrom the machine learning engine 140, those skilled in the art willrecognize that the transaction server 260 may readily be integrated aspart of the machine learning engine 140. For example, the transactionssupported by the transaction server may be implemented as an APIprovided by the long-term memory. In such case, the GUI tool 270 may beconfigured to query the long term-memory to retrieve the perceptsencoded therein for one of the camera percepts.

FIG. 3 illustrates a sequence of interactions between a transactionserver 260 and a client 304 performed to provide a user 306 with avisualization of a percept in long-term memory of the machine-learningengine, according to one embodiment of the invention. In one embodiment,the transaction server 260 may listen for client requests on a specifiedport (e.g., via the Berkeley sockets application programming interface(API) over Transmission Control Protocol/Internet Protocol (TCP/IP)).Further, the client 304 and the transaction server 260 may communicateusing any application-layer network protocol such as Hypertext TransferProtocol (HTTP), File Transfer Protocol (FTP), Simple Object AccessProtocol (SOAP), etc. Further still, each request to and/or responsefrom the transaction server 260 may be in a standard format such asExtensible Markup Language (XML).

As shown, the method 300 begins at step 310, where the user 306 invokesthe client 304. At step 312, a user interacts with the client 304 toconnect to the transaction server 260. For example, the client 304 mayconnect to a specified IP address and port number on which thetransaction server 260 is listening. At step 314, the client 304 queriesfor a list of cameras and associated presets 316. Each preset mayinclude a location and orientation of a video camera observing (orhaving observed) a scene. At step 316, the transaction server 260returns a list of cameras and presets to the client 304. At step 318,the client 304 displays the list of cameras and presets to the user 306.

At step 320, the user 306 selects a camera and a preset. The client 304then queries for a list of percepts from the server 302 for the selectedcamera and preset (step 522). At step 324, the server 302 returns thelist of percepts to the client 304. Alternatively, the server 302 may beconfigured to return a list of long-term memory percepts for a currentlyactive scene being observed by the computer vision engine 1325 andmachine-learning engine 140. At step 326, the client 304 displays thelist of percepts to the user 306. The 306 may select a percept from thelist. The client 304 may then display properties associated with theselected percept. The properties may include an identifier for thepercept, a label for the percept, a measure of how strongly reinforcedthe percept is, alert preferences for the percept, etc. The client 304may also allow the user 306 to modify one or more metadata propertiesfor a percept. For example, a user 306 may modify a label for a perceptto customize how the GUI tool 270 conveys information for the percept.

At step 328, the user 306 modifies metadata for a percept via the client304. For example, the user 306 may modify metadata related to a selectedpercept (e.g., a name, an alert rule, etc.). At step 330, the client 304sends a request to the server 302 to update the percept. At step 332,the server 302 attempts to update the metadata for the percept andreturns a result to the client 304 indicating success or failure of theattempted update. After the step 332, the method 300 terminates.

FIG. 4 illustrates an example of a graphical user interface display(GUI) 400 presenting a visualization of a percept encoded in thelong-term memory of a machine learning engine, according to oneembodiment of the invention. As shown, the GUI 400 includes a count 402of percepts for a specified scene, a list 404 of percepts for thespecified scene, a visual representation 406 of a selected percept,properties 420 associated with a selected percept, agent types 420, andactions 422. As shown, the count 402 indicates that the long-term memory230 of the machine-learning engine 140 stores fifty-eight percepts forthe specified scene. Further, a user may navigate through the list 404of the fifty-eight percepts (e.g., using arrow keys of a keyboard orusing a scroll bar 403 of the GUI 400). The user may view the list 404and/or select a percept from the list 404. Further, the user may alsospecify a filter condition for the list 404. For example, the user mayspecify to only show strongly reinforced percepts in the list 404—or mayrequest to list only percepts that include a specified primitive event.

Once the user selects a percept, the GUI interface tool may display avisual representation of the percept. For example, GUI 400 shows avisualization of a long-term memory percept, labeled “Human DrivesAway.” In one embodiment, the percept may be visually represented as aconnected sequence of primitive events. Further, each primitive eventmay be represented as a box labeled with a name for the primitive event.

As discussed above, the percept itself may be a representation of asub-graph of a neural network, where each node of the graph represents aprimitive event (or combination of primitive events) and links betweennodes represent relationships between primitive events. For example, theprimitive events may correspond to a basic units of behavior such as aforeground object being observed to “start,” “stop,” “turn,”“accelerate,” “decelerate,” “appear,” or “disappear,” etc. As thisexample illustrates, the primitive events may provide a collection verbsdescribing a suite of basic actions that the video surveillance systemcan detect agents (i.e., foreground objects) engaging in. Further,because the video surveillance system may be configured to classify agiven agent acting within a scene (e.g., as being a vehicle orperson)—the combination of an agent classification along with aprimitive event provides the basic building blocks for a percept as wellas for a visualization of such a percept used to convey a semanticdescription of learned patterns of behavior. For example, an analysis ofa sequence of video frames could lead to the following percept generatedfrom an agent/primitive event stream:vehicle-appear→vehicle-decelerate→vehicle-slow vehicle stop. Thus, thisexample illustrates that each node may be associated with semanticlabels describing the agents acting with in a scene, as represented bythe primitive events. In one embodiment, traversing the nodes of apercept encoded in the long-term memory, allows a clause describing ahigher-order of behavior to be generated. For example, as shown in FIG.4, percept number twelve includes five primitive events in an ordercorresponding to the order of an underlying percept in the long termmemory; namely, Human Appear 408, Human Start 410, Human Move 412, HumanApproach Vehicle 414, and Human Disappear 416.

Thus, as can be observed in GUI 400, the name for each primitive eventprovides an action which itself is associated with one or more agents inthe scene. Further, the sequence is represented by arrows between theboxes (i.e., according to the order of the percept). As shown, the GUI400 also displays properties of the selected percept, according to oneembodiment. For example, properties 418 for the selected percept includean identifier for the percept (i.e., 12), a label for the percept (i.e.,“Human Drives Away”), and alert settings for the cluster 604 (i.e.,“Never”).

In one embodiment, a user may modify the label (or other metadata) forthe percept. The user may also set the machine-learning engine 140 toalert whenever the machine-learning engine determines that a series ofevent matching the percept has occurred. Further, the GUI 400 may alsodisplay a list 420 of agent types. For example, the list 420 of agenttypes may include a human, a car, a bag, and a motorcycle. Further, theGUI 400 may display a list 422 of primitive events. Illustratively, thelist 422 of primitive events shown in GUI 400 includes an agentappearing, an agent disappearing, a first agent approaching a secondagent, an agent leaving, an agent starting, an agent stopping, an agentturning, an agent changing, and an agent staying. Those skilled in theart will recognize that other agent types and actions may be supportedby embodiments of the invention.

FIG. 5 illustrates a method 500 for generating a visual representationof a percept encoded by the long-term memory of a video surveillancesystem, according to one embodiment of the invention. As shown, themethod 500 begins at step 510, where the transaction server 260 receivesa request to view a percept for a specified scene. For example, a usermay specify a camera and a preset for the camera for which the userdesires to view the percept. The user may then select a percept storedin long-term memory from a list of percepts provided by the transactionserver 260 for the specified camera and preset. At step 520, thetransaction server 260 retrieves the percept from the long-term memory230 of the machine-learning engine 140. As discussed above, themachine-learning engine 140 may have derived the percept throughobservation of data streams generated from computer vision engine itselfobserving a sequence of video frames depicting a scene captured by avideo camera. Further, the percept may encode a graph or graph-likestructure which includes nodes and links between nodes, and the nodesthemselves may be associated with semantic labels, allowing thetransaction server 260 to generate a description of the events encodedby the percept. Accordingly, at step 530, the transaction server 260generates a visual representation of the percept. For example, thetransaction server 260 may generate boxes for each node of the percept,where each box includes a semantic label assigned to the node and arrowsrepresenting the relationships or sequence of primitive events encodedby the percept in long-term memory. Further, the GUI tool 270 may beconfigured to allow the user to view and/or modify metadata propertiesof any percept selected by the user. The transaction server 260 may thenoutput the visual representation to a graphical display. After step 530,the method 500 terminates.

FIG. 6 illustrates a method 600 for modifying metadata properties of apercept encoded in the long-term memory of a video surveillance system,according to one embodiment of the invention. As shown, the method 600begins at step 610, where the transaction server 260 receives a userrequest to modify metadata of the percept. If the user request is toname a percept (step 620), the transaction server 260 may set the namefor the percept (step 625). If the user request is to always alert for apercept (step 630), the transaction server 260 may set themachine-learning engine 140 to always alert when a series of primitiveevents matching the percept is observed (step 635). If the user requestis to ignore a percept (step 640), the transaction server 260 may setthe machine-learning engine 140 to never alert when a series of contextevents matching the percept is observed (step 645). Further, the usermay also request to modify other metadata attributes associated with apercept. After the steps 625, 635, or 645, the transaction server 260may respond with a success or failure of servicing the user request(step 650). After the steps 640 or 650, the method 600 terminates.

Advantageously, embodiments of the invention provide users with avisualization of data observed by a machine-learning engine of abehavior recognition system. Further, the visualization may provide aninterface used to guide system behavior. In one embodiment, a GUI toolallows a user to visualize and specify metadata attributes related topercepts encoded as long-term memories within a long-term memory of avideo surveillance system. For example, users may specify thatobservations that match an existing percept in the long-term memoryshould always (or never) result in an alert. Further, the GUI tool mayallow users to modify other various metadata attributes associated witha percept, including semantic labels used to name the percept or to namenodes or links between nodes in the percept.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A computer-implemented method for a videosurveillance system to process a sequence of video frames depicting ascene captured by a video camera, comprising: receiving a request toview a visual representation of a percept encoded in a long-term memoryof a machine-learning engine, wherein the precept encodes a pattern ofbehavior learned by the machine-learning engine from analyzing datastreams generated from the sequence of video frames; retrieving therequested percept from the long-term memory of the machine-learningengine, wherein the long-term memory stores a plurality of percepts; andgenerating a visual representation of the requested percept, wherein thevisual representation presents a directed graph representing the patternof behavior encoded by the requested percept.
 2. Thecomputer-implemented method of claim 1, wherein the directed graphincludes one or more nodes and links between the nodes, wherein eachnode represents one or more primitive events observed by the videosurveillance system in the sequence of video frames and wherein eachlink represents a relationship between primitive events in the patternof behavior.
 3. The computer-implemented method of claim 2, whereingenerating the visual representation comprises: retrieving a semanticlabel associated with each node of the directed graph; and generating,from the retrieved semantic labels, a clause describing the primitiveevents and the links between primitive events.
 4. Thecomputer-implemented method of claim 1, further comprising: receivinguser input requesting to modify a metadata attribute of the requestedpercept; and modifying the metadata attribute of the requested percept,based on the received user input.
 5. The computer-implemented method ofclaim 4, wherein the metadata attribute is a user-specified name for therequested percept.
 6. The computer-implemented method of claim 4,wherein the metadata attribute specifies to publish an alert messageupon detecting, from the data streams, an observation of the learnedpattern of behavior corresponding to the requested percept.
 7. Thecomputer-implemented method of claim 4, wherein the metadata attributespecifies to not publish an alert message upon detecting, from the datastreams, an observation of the learned pattern of behavior correspondingto the requested percept.
 8. The computer-implemented method of claim 1,further comprising: retrieving, from the long-term memory, a list of theplurality of percepts for the scene; and displaying the retrieved listof percepts, wherein the requested percept is selected from thedisplayed list of percepts.
 9. A computer-readable storage mediumcontaining a program which, when executed by a video surveillancesystem, performs an operation to process a sequence of video framesdepicting a scene captured by a video camera, the operation comprising:receiving a request to view a visual representation of a percept encodedin a long-term memory of a machine-learning engine, wherein the preceptencodes a pattern of behavior learned by the machine-learning enginefrom analyzing data streams generated from the sequence of video frames;retrieving the requested percept from the long-term memory of themachine-learning engine, wherein the long-term memory stores a pluralityof percepts; and generating a visual representation of the requestedpercept, wherein the visual representation presents a directed graphrepresenting the pattern of behavior encoded by the requested percept.10. The computer-readable storage medium of claim 9, wherein thedirected graph includes one or more nodes and links between the nodes,wherein each node represents one or more primitive events observed bythe video surveillance system in the sequence of video frames andwherein each link represents a relationship between primitive events inthe pattern of behavior.
 11. The computer-readable storage medium ofclaim 10, wherein generating the visual representation comprises:retrieving a semantic label associated with each node of the directedgraph; and generating, from the retrieved semantic labels, a clausedescribing the primitive events and the links between primitive events.12. The computer-readable storage medium of claim 9, wherein theoperation further comprises: receiving user input requesting to modify ametadata attribute of the requested percept; and modifying the metadataattribute of the requested percept, based on the received user input.13. The computer-readable storage medium of claim 12, wherein themetadata attribute is a user-specified name for the requested percept.14. The computer-readable storage medium of claim 12, wherein themetadata attribute specifies to publish an alert message upon detecting,from the data streams, an observation of the learned pattern of behaviorcorresponding to the requested percept.
 15. The computer-readablestorage medium of claim 12, wherein the metadata attribute specifies tonot publish an alert message upon detecting, from the data streams, anobservation of the learned pattern of behavior corresponding to therequested percept.
 16. A video surveillance system, comprising: a videoinput source configured to provide a sequence of video frames, eachdepicting a scene; a processor; and a memory containing a program, whichwhen executed by the processor is configured to perform an operation toprocess the scene depicted in the sequence of video frames, theoperation comprising: receiving a request to view a visualrepresentation of a percept encoded in a long-term memory of amachine-learning engine, wherein the precept encodes a pattern ofbehavior learned by the machine-learning engine from analyzing datastreams generated from the sequence of video frames, retrieving therequested percept from the long-term memory of the machine-learningengine, wherein the long-term memory stores a plurality of percepts, andgenerating a visual representation of the requested percept, wherein thevisual representation presents a directed graph representing the patternof behavior encoded by the requested percept.
 17. The system of claim16, wherein the directed graph includes one or more nodes and linksbetween the nodes, wherein each node represents one or more primitiveevents observed by the video surveillance system in the sequence ofvideo frames and wherein each link represents a relationship betweenprimitive events in the pattern of behavior.
 18. The system of claim 17,wherein generating the visual representation comprises: retrieving asemantic label associated with each node of the directed graph; andgenerating, from the retrieved semantic labels, a clause describing theprimitive events and the links between primitive events.
 19. The systemof claim 16, wherein the operation further comprises: receiving userinput requesting to modify a metadata attribute of the requestedpercept; and modifying the metadata attribute of the requested percept,based on the received user input.
 20. The system of claim 19, whereinthe metadata attribute is a user-specified name for the requestedpercept.
 21. The system of claim 19, wherein the metadata attributespecifies to publish an alert message upon detecting, from the datastreams, an observation of the learned pattern of behavior correspondingto the requested percept.
 22. The system of claim 19, wherein themetadata attribute specifies to not publish an alert message upondetecting, from the data streams, an observation of the learned patternof behavior corresponding to the requested percept.